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Forecasting uncertainty: the ensemble solution

Forecasting uncertainty: the ensemble solution. Mike Keil, Ken Mylne, Richard Swinbank and Camilla Mathison Data Assimilation and Ensembles, Met R&D, Met Office ESSWIII, 13-17 November 2006, Royal Library of Belgium, Brussels. Outline. Introduction to Ensemble Forecasting

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Forecasting uncertainty: the ensemble solution

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  1. Forecasting uncertainty: the ensemble solution Mike Keil, Ken Mylne, Richard Swinbank and Camilla Mathison Data Assimilation and Ensembles, Met R&D, Met Office ESSWIII, 13-17 November 2006, Royal Library of Belgium, Brussels.

  2. Outline • Introduction to Ensemble Forecasting • Perturbing analyses/models • Examples of probability forecasts • Application to space weather

  3. Weather forecasting • Today’s NWP systems are one of the great scientific achievements of the 20th Century, but… • Forecasts still go wrong! • 16-17 Oct '87 – still difficult with today’s systems • Less severe errors are much more common, especially in medium-range forecasts • What causes errors in forecasts? • Analysis errors • Model errors and approximations • Unresolved processes

  4. Ensemble Forecasts • Small errors grow and limit the useful forecast range. • By running an ensemble of many model forecasts with small differences in initial conditions and model formulation we can: • take account of uncertainty • sample the distribution of forecast states • estimate probabilities • Ensembles turn weather forecasts into Risk Management tools

  5. Ensemble forecasts Deterministic Forecast Forecast uncertainty Initial Condition Uncertainty X X Analysis Forecast uncertainty Climatology time Deterministic Forecast

  6. Adding perturbations

  7. IC perturbations: ensemble spread Ensemble forecast - spread increases, reflecting chaotic dynamics and model error Deterministic forecast, with increments each analysis cycle Ensemble spread is a measure of forecast error After each analysis, spread is reduced, because of new information from observations data assimilation creates a new analysis Forecast phase Forecast phase Forecast phase Time

  8. Model perturbations: stochastic physics The Met Office has three schemes to address different sources of model error: • Error due to approximations in parameterisations • Random Parameters (RP) • Unresolved impact of organised convection • Stochastic Convective Vorticity (SCV) • Excess dissipation of energy at small scales • Stochastic Kinetic Energy Backscatter (SKEB)

  9. Examples

  10. Ensembles – estimating risk By running models many times with small differences we can: • take account of uncertainty • estimate probabilities and risks • eg. 10 members out of 50 = 20%

  11. Example: Early Warnings of Severe Weather Met Office issues Early Warnings up to 5 days ahead - when probability 60% of disruption due to: • Severe Gales • Heavy rain • Heavy Snow • Forecasters provided with alerts and guidance from ensembles • Challenges: • Severe events not fullyresolved • Few events so difficult to verify

  12. Katrina – from “operational” system

  13. Katrina – NHC warning

  14. Courtesy of Robert Mureau, KNMI.

  15. End-to End Outcome Forecasting • An ensemble weather forecast can be used to drive an ensemble of outcome models, eg: • Wind power output • Energy demand • Hydrology – flood risk • Ship or aircraft routes

  16. Application to space weather

  17. Application to SW: power supply • Forecasts of disruption to power distribution • High degree of uncertainly • Longer timescales • Ensemble thinking can help! • A variety of perturbations can be applied to models • Inputs – the behaviour of the sun • Model parameters – known weaknesses

  18. Power disruption probability Information of this kind can be useful to customers • Critical thresholds can aid planning decisions: • rescheduling grid maintenance • load reduction

  19. Probabilities need to be explained properly Probabilities in context - a warning There’s a 50% prob of snow in London tomorrow 50% ? You mean you don’t know what will happen! Normally it only snows one day in 50 at this time of year - so 50% is a strong signal. When’s this talk going to end? Probabilities must be unambiguous and relevant to the end user

  20. Summary • Utilising ensembles is now a mature tool in operational weather forecasting • Ensembles provide extra information on • Uncertainty • Risks, particularly for high impact weather • We are learning how to use probability forecasts for improved decision-making • These ideas are being now considered in space weather forecasting • Power supply disruption • Applicable to other areas

  21. Questions

  22. Met Office Operations Centre Ops Centre forecaster uses the ensemble to assess the most probable outcome before creating the medium-range forecast charts… …and assess risks

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